{
 "metadata": {
  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
  "example_name": "Editing model parameters",
  "include_in_docs": true,
  "priority": 5,
  "signature": "sha256:179fb20339497f5e64f6fbeb57987f27a962b7ae6d940c8fede2631aba9bffaf"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Net Surgery for a Fully-Convolutional Model\n",
      "\n",
      "Caffe models can be transformed to your particular needs by editing the network parameters. In this example, we take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully-convolutional model for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional neural network (CNN) structure by dynamic programming in the forward pass from shallow to deep layers.\n",
      "\n",
      "To do so we translate the inner product classifier layers of CaffeNet into convolutional layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding.\n",
      "\n",
      "Roll up your sleeves for net surgery with pycaffe!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!diff imagenet/imagenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1,2c1\r\n",
        "< # This file is for the net_surgery.ipynb example notebook.\r\n",
        "< name: \"CaffeNetConv\"\r\n",
        "---\r\n",
        "> name: \"CaffeNet\"\r\n",
        "4c3\r\n",
        "< input_dim: 1\r\n",
        "---\r\n",
        "> input_dim: 10\r\n",
        "6,7c5,6\r\n",
        "< input_dim: 451\r\n",
        "< input_dim: 451\r\n",
        "---\r\n",
        "> input_dim: 227\r\n",
        "> input_dim: 227\r\n",
        "152,153c151,152\r\n",
        "<   name: \"fc6-conv\"\r\n",
        "<   type: CONVOLUTION\r\n",
        "---\r\n",
        ">   name: \"fc6\"\r\n",
        ">   type: INNER_PRODUCT\r\n",
        "155,156c154,155\r\n",
        "<   top: \"fc6-conv\"\r\n",
        "<   convolution_param {\r\n",
        "---\r\n",
        ">   top: \"fc6\"\r\n",
        ">   inner_product_param {\r\n",
        "158d156\r\n",
        "<     kernel_size: 6\r\n",
        "164,165c162,163\r\n",
        "<   bottom: \"fc6-conv\"\r\n",
        "<   top: \"fc6-conv\"\r\n",
        "---\r\n",
        ">   bottom: \"fc6\"\r\n",
        ">   top: \"fc6\"\r\n",
        "170,171c168,169\r\n",
        "<   bottom: \"fc6-conv\"\r\n",
        "<   top: \"fc6-conv\"\r\n",
        "---\r\n",
        ">   bottom: \"fc6\"\r\n",
        ">   top: \"fc6\"\r\n",
        "177,181c175,179\r\n",
        "<   name: \"fc7-conv\"\r\n",
        "<   type: CONVOLUTION\r\n",
        "<   bottom: \"fc6-conv\"\r\n",
        "<   top: \"fc7-conv\"\r\n",
        "<   convolution_param {\r\n",
        "---\r\n",
        ">   name: \"fc7\"\r\n",
        ">   type: INNER_PRODUCT\r\n",
        ">   bottom: \"fc6\"\r\n",
        ">   top: \"fc7\"\r\n",
        ">   inner_product_param {\r\n",
        "183d180\r\n",
        "<     kernel_size: 1\r\n",
        "189,190c186,187\r\n",
        "<   bottom: \"fc7-conv\"\r\n",
        "<   top: \"fc7-conv\"\r\n",
        "---\r\n",
        ">   bottom: \"fc7\"\r\n",
        ">   top: \"fc7\"\r\n",
        "195,196c192,193\r\n",
        "<   bottom: \"fc7-conv\"\r\n",
        "<   top: \"fc7-conv\"\r\n",
        "---\r\n",
        ">   bottom: \"fc7\"\r\n",
        ">   top: \"fc7\"\r\n",
        "202,206c199,203\r\n",
        "<   name: \"fc8-conv\"\r\n",
        "<   type: CONVOLUTION\r\n",
        "<   bottom: \"fc7-conv\"\r\n",
        "<   top: \"fc8-conv\"\r\n",
        "<   convolution_param {\r\n",
        "---\r\n",
        ">   name: \"fc8\"\r\n",
        ">   type: INNER_PRODUCT\r\n",
        ">   bottom: \"fc7\"\r\n",
        ">   top: \"fc8\"\r\n",
        ">   inner_product_param {\r\n",
        "208d204\r\n",
        "<     kernel_size: 1\r\n",
        "214c210\r\n",
        "<   bottom: \"fc8-conv\"\r\n",
        "---\r\n",
        ">   bottom: \"fc8\"\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The only differences needed in the architecture are to change the fully-connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make sure that caffe is on the python path:\n",
      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
      "import sys\n",
      "sys.path.insert(0, caffe_root + 'python')\n",
      "\n",
      "import caffe\n",
      "\n",
      "# Load the original network and extract the fully-connected layers' parameters.\n",
      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
      "params = ['fc6', 'fc7', 'fc8']\n",
      "# fc_params = {name: (weights, biases)}\n",
      "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
      "\n",
      "for fc in params:\n",
      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fc6 weights are (1, 1, 4096, 9216) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
        "fc7 weights are (1, 1, 4096, 4096) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
        "fc8 weights are (1, 1, 1000, 4096) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Consider the shapes of the inner product parameters. For weights and biases the zeroth and first dimensions are both 1. The second and third weight dimensions are the output and input sizes while the last bias dimension is the output size."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load the fully-convolutional network to transplant the parameters.\n",
      "net_full_conv = caffe.Net('imagenet/bvlc_caffenet_full_conv.prototxt', '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
      "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
      "# conv_params = {name: (weights, biases)}\n",
      "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
      "\n",
      "for conv in params_full_conv:\n",
      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (1, 1, 1, 4096) dimensional\n",
        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1, 1, 1, 1000) dimensional\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we need to roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices.\n",
      "\n",
      "The biases are identical to those of the inner product -- let's transplant these first since no reshaping is needed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for pr, pr_conv in zip(params, params_full_conv):\n",
      "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The output channels have the leading dimension of both the inner product and convolution weights, so the parameters are translated by reshaping the flat input dimensional parameter vector from the inner product into the channel $\\times$ height $\\times$ width filter shape."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for pr, pr_conv in zip(params, params_full_conv):\n",
      "    out, in_, h, w = conv_params[pr_conv][0].shape\n",
      "    W = fc_params[pr][0].reshape((out, in_, h, w))\n",
      "    conv_params[pr_conv][0][...] = W"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, save the new model weights."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "net_full_conv.save('imagenet/bvlc_caffenet_full_conv.caffemodel')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To conclude, let's make a classification map from the example cat image and visualize the confidence as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "# load input and configure preprocessing\n",
      "im = caffe.io.load_image('images/cat.jpg')\n",
      "net_full_conv.set_phase_test()\n",
      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
      "net_full_conv.set_channel_swap('data', (2,1,0))\n",
      "net_full_conv.set_raw_scale('data', 255.0)\n",
      "# make classification map by forward and print prediction indices at each location\n",
      "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
      "print out['prob'][0].argmax(axis=0)\n",
      "# show net input and confidence map (probability of the top prediction at each location)\n",
      "plt.subplot(1, 2, 1)\n",
      "plt.imshow(net_full_conv.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
      "plt.subplot(1, 2, 2)\n",
      "plt.imshow(out['prob'][0].max(axis=0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[282 282 281 281 281 281 277 282]\n",
        " [281 283 281 281 281 281 281 282]\n",
        " [283 283 283 283 283 283 281 282]\n",
        " [283 283 283 281 283 283 283 259]\n",
        " [283 283 283 283 283 283 283 259]\n",
        " [283 283 283 283 283 283 259 259]\n",
        " [283 283 283 283 259 259 259 277]\n",
        " [335 335 283 283 263 263 263 277]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f283815ce50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P/fif5nv/qe/hg5//WQaLoKBJUC/M5oqGjBNHPybMpDJyq/KCK9VpXB1/gAkinmQjZXKY\n5jHjkpJLnmQcI+aROFbgzluHlYFSlNwr4xBprOHegzd4+OhN1AspJ+4fn5F7QVYrLrcbdOMJTpg/\naFjcOyAjzO4VTk+3SPQUjTUiKK0ZtjLF+bsaoSMFnOEaoRWHOI94oe080gjiQnVqp4INkZwLKUa8\nKqgQzTASOTtKcZWVJyNLoekCR4s5jQt0bUsKLzUZ7YUEil/8D/7KVfvBD36BB+984UPnZPPV34BS\nzNWRKVJjmaT6kFBD1BAtqMu4orXzLMIUuwsYzuUaK+5qnLnzCe8SXjNOM85Na82olGkxRKZXFJtC\nYG+aTHeklBvt/cVRQ0b327tY8901bsePK6W27XqfI19d8frcPMWbK2qlvmeY1ns3LFKj4kQwBbQG\nUJgTrExBEVPIMgZSCnipaQme57Y/tO2oUWJuajvATdsvYt9smk/5WbC/fb39dZ7AVwfmpbKtIoZQ\nKJpRU5IlnBrBG0EyPhScVwRBW8hJ8aPDmbLNyvY8cioXYMr8IOG9RwqQEhYTKVZNWXxmvdrw+P1z\nXnv0JmWEg8WCeFQY4xacxyQSLSFSuPdggZ+NHN4Dv/BkM+LgKVrjwH0bmHUtzjuwxKBxisTxOIu8\nNnud/v2n+KZl1gVSyRwe3+OX/qef5t/80/8xf+/f+WHeHwpRCmkozJqWphFElOQcMa5r+CGVtQs1\nusdEoViVPKoCM3WABYtQtp7YF0Kb67drhmQjp6E6Lr1gfXWojjkSeiH4JceH93n99Tfo45YxjiyX\nB/SHAwffcsTXto85/UdnzJdzZgcNYeY4PDzk4GFifs+xuRgwa0gy4KaQ0JhBrT7pViLmDN86pBGc\nF5wP+MZjFMTV3IKUEyXWSJU4DpQhIimBZkIjxDwiqqgTvNb3Npu33Lt3xMHBHOcUVz618ILn2cdm\nNwP8tn//x29sp+f8AFOpYF5sD7xEr3FqwnRVQ7VgO0DfDcYMxGoXoD7hfa4g7hPOVTC/WmvCuYxq\nqYuU2kFIuQJ0rrqS5y+3QXwH3ru1J31oDVDqHU7gLc+5Sl3MbgJ4mbqQ3f9HK4jVPq7616bO5noD\nVBCdYgVqlO/0We13AlRfUxLIgiWwLJDkCryv11Pb1bU4uQZ0LxOgP+cpkQ81nnMMrpFZPvJ0+IFp\nmWz7nz7vJOBVauYxgdMqEugUG66Gk0KjhbYxfFNoGiUoqE69ag70fUODoCr0l5H1KmKcs101NRa7\ndZRipGSksQK09wEj8d6TU9784Awnc0Qv0VbREFitzhnHLSKR114/wrnC8sTTLBODbWtGaKxJQrg6\n5FfnaNsOr54urInbEdFCk4XjRUd/eUnjW1TAiUGOyPyA3/z5v8qf+qN/ij/3Mz+NDIlcBtq5oprB\nHCll8jiBUg2Up5jRzlvefusNnp4+5dmzZxgOLCNapSopDoqiMWHZ1c4gFazUZCQTY3kciD6wPevJ\nvWEWaNsZ8+WSguFDAIGuaTlYHhGJ3H/zkHKesWFL8Yd0y47usMWFOdo2+MWGtInkAhJ6WudJuVSn\ncjC64wOavjDOCiXnClQIpdT4fAPGVcTEaOYeAVI/MA4DkuooTIXKVJuEoOCMtvHcv3/E8mBJ21XH\nMcNLlVn+HvAd1PLO7wJ/gprdfMNS+fjwyGzuarkCtCtmzgRKEytXwVyeoruovwPsmmVPzNz5jJtA\n3Lm8t86o7i97QD4h5G0cuQbyD7PyHYjvs/H9ZZ+Z2wTIxfZAHbm1Xd/7PlO//X/OSn3GuYWVu45P\n7SqbvKZr1FFtxcv6H2ZT6PMOzNMOsAULgqVp8TvQntj9DWA3bGLk4qjt/fu50bAPA/Rzh0DGRyD5\nN2SvkJkXzFJNHHKOIg7RjPoqsThnzDroWsG7+oA7CTXZxAWclzpUd57V5Ui/qT/ys7MeVGjbXRed\nMRVoR8wKm4tL/v4Xf42jwwO2sbA4XJDHnvVqTc5CaOHo3pwuFPw8U9yWHOsQTp3iFDKZTELoUPUg\nwrzrYFYzHA984NsffSvx17e0xy0lDbhmRhFh6HvKB2t++J0/zk//jZ9mCEIjnmXXVv08wjgkSjKs\ngFiNL3fe8fC1+5zcO+D1Ryd86atf5ctfeRewSbe2CnKjkbaK6xqcS+AL2IiIY37Y8vCN+5Rxxge/\n+ZR3v3aGWyr37t9HUMYUyTlydn4GNrBczNgWR/P6Q4bHhcv3tqRVxr0eaGct4j0PFi3NecPF+bqG\nSxaPiEBRRGHmF9WJ7TtOfAVcJ1VXvzi/5OJiO0liwtCPJKmgFfuIpvoDNiu1bEOofZtIxgscHhyy\nPG5oZp7QtSBG6l/qU/tC2c2xfPzPqhR3zcztGsBqrkWVWWSSWdRNEoNLE6hWiUml4CYwdxMzd1Nb\n97d1f9kB+W7ZSSw7lniTqe/B6h5X/vogvlt2Ektl3VN3sN9Grhj57lVunnvdhTirjvorZo6hcHX/\nItSyIFMnWAHd9mSJqTiI2BUzr4tiWShJMK/XoO6VEgSLE5A7rX4qL1V6cZMMk7mFw5NEtrd5k3nv\nsXHh1nmfzF6dAzRDkfpVGb4+QNPQ2YVMECEEI4SMOsFpPUed4J0Hb4ivGYpFBuaLExofGGLh8ftn\nrE7HaVjpkMZwTQsqpGFgvVmzSWuCeoZxxebynPVmhYoyWzbMF4Guy2gD26SIOXK0ynC9kMbCMES8\ni4ShpW0LzgvdrEVl5K2TexzFh1jzPpSqazfzJePqHOccz/oNh3//7/KHPv8D/KVf+Bk676uzUoQB\nY4gJDaCmlVmbo1k4HpwcMm8DB8sZsTzi/fce16gbSZVFYBQ8OtZIIKjDdBBmBA6PHvLGwzdYhCO+\n5fU3+PUvf5GwNE4ePkJdYdz0XFxe8v7X3ufZ5SkHi2PUCVGE45MDLp+esnq64vLRAf5wTnMIs6Zh\nduiJ2RPPyvTsCk0j+NwgBNS1BOfAwsQEBe8L9+8f07QNz85WpBwpyWq255gYtiOuZGYdLJczZvNm\nCmFMWI6EoNw/useDB/fp5i3ee4xMLi+VmcMLZDe/CDMvtgM2dyUl7KATmBCrSizVYQL7UoiKUqRQ\nJF+B+TWoT21XUE04V6pePgG5akbFbunm1y979fI3AP2j5JY8VVH5MKgLNjHrm+C8Y+HXAH4T3J+3\nv4J5vbMPM3MBrakIO2ZuttOz5eozNSZmjlGSYkmu10GxqBQ/AbkviFOK1ytWXnwldMXZpJtPr7F/\nQzsgvwHizwHwfTZ+BfKfDNFfbZw5kFOq6YoFcNXZV3L9MgTDJNd0eclgkEWwAK34Gv1iynz5kNls\nyXw+p3Ez7t17yq//2hfZnCcsC06NPCbEK9J4ZrOGbXpKe3hMXzaMOZKGEStKCIp2UxieKNuYiEUZ\nk8OZIMUTrDD2kXVa46UhuBZaR84jzaJl9eyCPFwiOHT6AjcXFzTdgmHcMpPM4/ff5cf/4L/K//x3\n/zfCoqtgZIo4qRpoywTOBsXY9iNt1zGbtzRdw8NHhzx644ivfuUDrHgUKFNihZWCxCozhcYjAm3j\nOHlwwFtvvcXxyT0uPrggHCqjRDofKCnx7INTPnj/a5w9fZ9nq8eksTAPc5LCoJlsSu6FD750zuxo\nSTtvQcG5htl8ZLv1DJtEzhmVjGjA2yQTqFZNe4p+yRmMwmIxI0vhybNnNXJlzPSbgdQXmuCYBY93\nc+6fPOTg4IihrMhpjTpl3nXM5h1+5hCvpO2GPm1f2SO9sxcC8zIBl10Dm12x8kkGVpuCAWrHvIPC\nMo3DTJQigrqJnbuM+lLXk0Z+JbFcAXqepJuJmVPZ+ZV+w77E8jwQvym3uKk72gfyfafobfepIVPt\npB1YT/tNboC5XbH2er6zcoV5Yrv7vMZM2WnmxjSKuf6sd0qLTaAv1KS4EpQyrS1N20koXhGvmDfE\nG8UpxYM4rSTS2Q1n6I0ekP2bug3ixs1ec297//+/SXuFYF6TR8SUYkPNgDRHHgrZKaVLtR2qtz6X\njOLYFdMK4mi6huQSzs85PrzH4cEh3rWEriWmwrtferdGqASPZSGPNcri+HjB1gaWsmEYB5giKcZx\nRL2rei+JnDYVWMRhWVFzlS1HITjHdkisLrc0GghNRsWz3RY+49+icYaft+RcEFXGYSThODlastkO\nACzjyO98+G18lTM0GM4pIxltM96MnD0ksKyUGHlycc6br5/QzB3jpvDg0RGDjcQxk3Mk9tXxmLIn\npkjA1QqFojXeG8+9z9zncLEgNA1RI/PG8brr2Jxu2PQb1qfPOHv6mMvhjM22597xG9ig9CVSxJGG\nSP9k5PzJlm7pyH5G0wKhxo4nMlhBaDAxstbqliod/ZBwsutgDDfpnPN5yz2OOT07YztEygAaIZgj\nnMy4d/SIWXuEdw3L5SFNC0O5xKsxO2hoQy1MNsaRPH5a6X3fvMUXAPMaWSHXgGY1qusKobRKCmgN\nPXVXAF5QqdEa5LpWLRXQfb4C9rrea++Y+Q2Zxa6AfJ+/wm1Av5ZXburmOzaePiSzBCI78W8HzDsg\nv17rre2P3p9KRq80c7si5DLpLqLTrdtNb/MNB+lOiqFQgqPkHYC7a2D3Sgl1JF68XUW2VKcnFCfI\nBOhXoH71QvZhIH/e/ucNg/bX36S9MjA3TVAqz6CAiKOkWv9jHGDohdAZ2hScqw7QaPWx3r3r4AXB\n0wbl8HDOw0cP8S7g25Z+NbJZr9BWa3gSuYbw5Y6D+QmHiw6VzHY7sN0kiIXNdkPTOtxWEV2TcmaI\nU+hY9gQa0iZiGVIxVGr336dIiC1taJi7Yx7c/074aiSOQtt1lBLpupblyX3GYUsILZvVmq/83/8H\nf/KH/jh//m/+RZKLFDPUJVyjkDNjX+qPXEDF8+X3vsLnPveQsMlky7SHjvuyRNST4kjKmc0qsd2M\nbMsuRG+KU8+Fpm0I85bFckYYYNkc8IXykNdlTi+F98Yn/A+nv0IcImnIDHlN2J6hKZAEokS2GWzI\nfPVX36M7fpsZBQ5nmEXazjP2Hkh4UWTiZuvNhpx7xrFAKrTzhuUs0DjHmCLOOeadIy+WbM4HbEik\nPqNOSRHEGsgNjkDQBrOIqic0npQKkUQphXGIjP2rB/NkL/CzMqmywOTUvOaaXLNzraBluyG4XB+j\nMAE6qCvIDrx3wH57346N78stN5ygXN3BTjPf181va+d6g5Hf1NB37Lx++9ddAs8FboF9oN87xt45\nbopmuZac9294+mz0+vgVpMv0TqR+XpavwTxPIJ5ToSSleEcODkm1NIfsg7mvoYniy3VI4n40y41h\nwh6Qy/V3d8XEn7vvBR6sj7FXB+aWK5ATEAqWS3X2SdW+8iikVEgj1WkTqlaWLdWiVNQEHRFPUaNb\ntiyPF7TdjKEY3cGMxcEBeaolUgRiGpg1B0iZE/QAykiKT9lcDKwv18TtAKp470gWa11xaRgHI/WR\nbYyQofEBGsBqXRjnA213RNPeYybHdPkBsVziNJNyJLiOTb8mnp6R8ohqYtm2bFPke978AvMusVFh\nHHuSjahkxpLJBrXYQWG2FNqu5ctPvsKj+/fwzjg4XiBd9Sc0YQaWGfrM2dkl55uWVXqG2bqOglSJ\nMbI+PWNWlM9/bUHzeMnZ2Vf5VRFKHvn8d30X3/r+Ie+XwFmpWv359hmlD/SXMGRl2Ga8CasnhfX5\niLZU3Va1RihNeq5ZZeAqjuVsydBnLlc9Y8k8Oz+lj54HDx8QnCcPNSa+dQ3z0HK5voRB2UqPvR84\nmq9oZEYXjOQFS4USjOxg6HtScKQUuXi2Yr35lIp4fAJ7EWYuU3jh1fZuQ66XWpmgXGPE3jHU6jWU\nCtpq07o8f9uV6kyd5JW6nnRz7Pr1AdhnwOVq/TydXD/C+blj5teAzF77JrCzNzp5/n7BTw7QGx/R\nzvmpcKM7nP7IrtObKqmy+7zEyMHhUgV0TWUCdkOSkf0kp3h3E8yn6BaZ5BduO0D3v58bLHwfsJ8D\n6P+kM3OozrmS09TJytUTXkyICcZtInglSy2Q66U6E6X+MyVOIYsiqBhWelSbWsCqdTjnCKHBewXv\n2PSeo+UxeRDSWiiDY3tu5NHYrvta6S8aWwrOe3IspFjZ4bipunvbNKzHEW+C62p8tHOKc57l7Ahx\nDYumwRCEBMymAAAgAElEQVSGPpFcQWZG17XgA0eLY9KwwYYNxeDivX/EF+59Bz93+iv0JRGTkbeF\ncZUZL+tPeT4LNbU+ZC6fneNVmS89Czfn+OSEVBJijrZdcHCkzBYLZpcNzy6UIQmp9Egp5GHALguf\neTdhH/wm6yw4P4OUkJz5lV/4Zb777e/ib7z3i2CBlEdccUhTs16TRFKu3n40szq/pD0+YFDDi1Jy\nwUqNSDBxtIsZh8sDnHpSMg76yJMnT9CxwXkFjSyP7jGsRsZNj3OG98Zs1rIde0yMcXPB4/fexSuM\n8YDFsmd26PCzhBcQMpvNwDhGNqsNm/XLDWd5EXsRzbxGZtiVBnzlhLTJKalWR6w6bU+FykQNKZME\nU+o1rhOL6lqugPz62NU5Mm2L3ZJa4JqNf1hmuXbR7hj5bXb+4WUKPJ6k0T0hZ49111HJteT0oe3p\n/Gz5mmJzi3nLbi01YWh3fBrV2kQQy5RwJWKk4CjJIdEjvqC+kLzBrmDeHhuv0SxSfW6TxMLkBL0S\nCj4O0G+D93PP/2T2SsHcEETrjDw18bMOy1QKZTTSWBh7UOfrMM8XnCreBYwK8MUS8zCvvS0jfb8h\npZ5UBgojwTmcD0ijtCnTzmaQPOMGYhm4fJq4PDsnDYmMYT0QDYJAFobeyGMhDTDGQowjIoWDrkNK\nrW0yxrq40LKcnbD94BzZDFPmWUZoKOLYrFaQjDFHWjUQz+rpJf/SD/4Yv/xXfoqL0RjWA9t1Zrs2\nUqyZplkLxTJFHEMqPD19guUFXmE5O8C5lu1QoAXxynzZEWYPCI3w5HTkfLNl7uc4nfGFd+EkRUbX\nEJoGJw2SI7F3rPo1JWZcycSSUGo0TdFCdjW5R7QOSxvnUKsdBI2ivqkTR6T6gwxtQzPr6BZzVIx5\n8BjK8mHD6bMzSs54q7Vb2oVHSuDy4gIs4zw4LzRtILTCdljzxS/+Ot3C8/C1+xzHOYtlw2LW4hsl\npchmPbBerUnbfzIcoDLFTVcdeM9FuGNvVCBXs3qu2FUHoFqu2mITKOuk434IwG8v1wAue6z8Sntm\n9/r7zs8PSywvEp64c9xeqdy2a3OFstftaz8CcAXsu07gCsyvPp7pZo0Klrr/GhOw78BcZfotyhXK\nq/dkX5BgdUKXiWnfYN1ewOXrWHNXKE4nvZxrQL8eKuyBtt0E6hsM/WMA/pu0VxeaOD209bsoU8sQ\n0av3mbMSU4beEbKQPbRTmVlz0DShJtpgFMtst2tSiJyvzjhfPWazXeHKbIpQUkLjWW8uWSwWrC8S\nxcHqYkOMVZ5QVUoysha8B8ORByNuM+OQyLnKPe3M1ZKwZsRuBFU2zYrzizOO/JIuNmDGMCZKW1ht\nzmnaOccnD8kl4QuMY+TxkyfMGsebaeBhOeZXV08Y19TaLKWAKFaEEgM5CsU1xJKIlz3LZkZZGOM6\nkgqs+h4pC2ZLEBfQLHgXePjgDfr3NxzNHvADdsLbesxFWtO5OaoB7xrMDL/oGNLIzB8g1OSjDEim\nJkdYuYoGsAztvMF1hjipWbcqpBRRcXgf8L4W4yoamR0sCJMcFuyQMAukPBC3CY3Q+Tmn40jRzMFJ\nU19DC93cEbwScyYOhWyRy/UF7UJBIkKmpSGnSBxGUkqszodX9Uhf2YvILEqp8c5W0ElKU8oUOld/\n+BXA63lCjWpRK1f/K1NbxKYY64nR78WoX5UE2Ekz+/v3kobgNlncB/LnAXr+EKBXrfwazN2kQdy4\n+hWg7kB7b9/esZv7qx9ip5lf36fdzMGZQpuR3XXrvl37ah9GajKaPBILGnzVySeZhQgWpBaw84L6\ngnmheEN9oexkmMkp+pHgfQXaPF9yed7yCeyVgbnTMg2fbh+53mEpk0YlW6IkhwbD/DSpQxOIcaRp\nlFQSl9tzaArab9hszun7S1LuKcXQvMDFWphqtb5gebCkjAUJifXQk7eJnGvWZI3Xhr6nVgaMkMaI\nFcNKnSUHanlXJJOGkWEY8Xge8y5+dHxP+21cppGx7xGFdn7CsLlEfMfi6ITXH32Wi2dP2Jy9TyyB\n9XuP+dEf+FH++n/5kyzaBfSGtP3k+KxD6tRnct8zDlssjrSN48HRfYbNQD8kfvO9U9r5Ew5Plhzf\nO6RtA86UUoS3j97i28MJ333vuxg2F8z9Ag0NTj3OKeoaFM+8ixzM7zEUIzhPJIJonaxD6wxD2axO\nOdd6XOdR73GlTs1nU1JH/WwKMQ8M0dPEhmYZ8M5hpdA1gZKFGDIkQWMt4DU/6uh9zyIUwnw+TcUH\ncYwM/YjkBrOaZet9Wye6QKAIaexJsUB+9ZNTpBdIGnIUjHylJ++yz41cv3d2YFUB3e2BqLNrMFUr\newBOBY1JF67gfXsf1wAu7DHya6fnh6WW50Wz3I5o2QF6vgnmto9Qe+09oL4J5LeP1fbOAXqbnMNu\nDDGdekN3ryeWSYcpV9KNodFIwZA9AL+SVgJXyUI6LeYVvWLmTMycW8ycPca91/564H67/Qns1U1O\nASBGcYaliY1L/QJ0AkoMYixTDLUhSciN0JgQSIg25AyQ2A4bynnCa9V6m9bTzj3jZSSmLUimiJHT\nyNOz97FQww/77YbUbxFzpCQwFgzHECMURy42yUAZpCBaa20EBfWJVAb6bSENp8SNcZAWNJ/97SQC\n6hLDZkPfbolWaC1xcXHBdtvTOENcw3pzyeMnkd/2z3wf3374iC+dP6FrmzrZhUvEWMh5ZNikWupX\nCqETNpstJSqDFVLKXJxecPmlU45PlizvLXj7rbdJkplReNDc48de+31sVxeIBtpuhrmGzgfECaQe\ni4XgApYKQRu6VnFlxUiqSVuN4GcJaaB4T3vkCV2o36EBpeClQbyvsz85T+sDcQ2n41MWizldM6dz\nRtKOYbtFSyJaZNxuKRYJMyEWaKYJRyxTJ722ydGVO9qZUcqI9wtECzkPpKIUAttNz3b10pOGPtaS\nvUBoIhlFJva6+73bla6M7BjxHojf0KN3USSp/vMVSLPrCa6BZH9bJ4K4zySnO9rZTSC/1spvJuCX\nvXu4GZ7objBzJr/AbfC+xrx9IJdb5+zS8pPVJKSbzHzvM9tdbLfaI4m7dtndi+xAvMosEg0CdYns\n1WWpIG6+UFyZJiC3qqs7u+kAfS4r32vrrr377D+CnX8Ce3XRLFRnp5venDHNPKSCSZ2JHZm+yDSx\nA6eUXGPC5+JZHhuLZQCFGCMlF5wbUbRmSY6Z87gmxcSQypWEMw5rxn7AtLJvwVOyIpk6IbPVWWzi\nmJFcMG+4UOoUbmSKJbLWDDzLA3mEvBXcsGZ+Xxm2PR4hIog6UhaOj98kAfdP7jOMI6XUmizDZsWD\nowP07Jw/9v1/mL/wt/8az+IGZ4Y6x+V6YOwTnW85ebAgdKsa6RIjfexhrJqxWCb1I6fvnXF2tsLl\nlrBQ3uge8mPf+U9zuT5DzNO0HUUDTjwqHssjIorzgeAdkjOH8xNKWbFJI8nKNG9nxoqiQQjHSrME\nVNBGKWqM25GZzkGM4AOHBwcUhX67QWLDxdOe+eyEEDyzpqb0b8qWNCTUeZyf5v+cCY1vak33bWXl\n/ZixJHShSjpd12GW6qhLHaUk4pApI3VKv1dsLxRnjuBE9n7/OxlD2GkHu/3KlLq/Y7+SrgFU8kcM\n8a8dg7eXj9zPDk927Hyfld/sWG4C+c2QRL/XhmuMei5Ht1vtHdAXboC+u4Kq6Q7lWqa5aiM32895\nJ/UcrkHccwvEmSQVmTJBtZaRnoB8F1/+Yc18D8D11vaNOPfbQH5r+xPYqyu0JeAq556cnzZlfQqm\nBZHrL22qfA7ZKERycXRLT9u1PLh/hJmx2W4oxQjOEZqA84XZQST3mfXlSCxGjMPUO2dMM0KtoS7e\nYSaknJFSZ/BxuabVS7FJigB1dYKLokKWiNMBESW0gbwWNpsVbCIxxim1uGrwxaoEfvLgIZeXl8Rx\ny70HDzh98ozFfMH55YpnTx/z+77n9/Ozv/B3mKeWbCMXvsdLgGHFW68/IDYXHB3P2SCsL9Zs4jPy\npqGYMcYeqHVrxtXI5mtPmR04/uU/8sO0gzGUCqAFx7xbkFOiWERL7UC9c7gQoHHo0Zz5aJQ+0W9H\nyFXbFXGIh6OjJd3SQyPVUWwF75UWx+FswaxbYsGqBj+bk1Oh34xsVlsO2iVSjG7eMQ4joQ2UaDgZ\nEQHvhWKOftySciH2I3mAMho5gPMeH/w0f2otqJZHmypDBlJZvczH9r8B/gXgA+B3f9RJLyKzUEf7\ne45IRVFMCuxG3FPEiVJrsDjZAXjC75aJmdsEGjerxMpetdjrtu2DPddBiVda/dWyz9BvSi3u1mjB\nP8cB6sgfAvIrBX2fnV+t7aoy4u1jVyz/Fpjv3s+1OFT3Fa731eMTmO9i+EPVyK+APHBdcGuXPOQV\n9Q6dol10x86dXWvm+zKL7rV3IL6Tua7OeQ6Q7//fJ7BXmAG6S1avYFBjzKcnfHJWlGLTl1WdgYhM\nvYAQupYmNKhTFvMO55XLyzWqjhACOSe6pmXoRnToKdtILJmYDYfhG0ENGjd5SJzUSZvVanXebGgW\nSkm1BK0qoat6musSzlEzGItRnGBqEIUHBye03Zxnj1dYGhGUdr7EtQ0pRZpZy3bY8Bu/9it085Zx\ne0mat/T9yNvHh/yOkwf0uXBvecDPffkf8tuOGn5p/ArbdMqb91o+8+abfHX9RSw7hn5L2uQ67JvC\nKUsyXPIcmeNP/q4fZAEMcQR1uGZGM5uTpkk9nBqWqoThg2K94WaBb3n0Nl+9fIp64WLYkPOIUCNZ\nmjk0C8HNBBcKxRlpKByGGSftnGU3x/uWIUeiCEMcq1SmPWnTkw/nmBSaxrNYHGCjMUqtYS5WZZU4\nDsRxZFw7hnVh3ERmszlNq2jYEQCllEyORr8ZWF0ObPtSi76/PPtvgf8c+Omvd9KLRLPsfrz7YGnT\nqNSmH/ouDrwm+GScZLxUZh4kToAeJ1YtN4FtCko3ucVYRa7OqfkaE6Awta9u77bMYreA/MOAfjui\nZQfmV0GJxlWxrKvXuAHa07Fy3d6BvDd/HWfPnigk+8Au18dvgH09v8hOa59klp20MgH6DsgrM98B\nep7Yubtm5lfhidTlCoxlj31zi6XzHPlLbp338Y/N17NXKLPAjguI1DosuQiqilEr5dVUf6sVC5Fa\n6EqVnMGpRyXAlPrbNR2DHwnqa5pzFrx6VF0tteugxIxJ1cDTWJBGCU2dpci8oqUmL42xzmakOCwU\ntBV8V6AD1xWaUL3ZJg5LCsmhCXxoOZgf47xDqCX2jg5PmB/cQ31gs63ToC3nS7YXZywXC778la8R\n/AlDGsibC37gs5/lbEjcbxryesvWw1thyalsmR03vP3obeRsQPt36e2QJ3LOZhw5aB3OOrabzKNu\nzh/73j/Ad7/9Odbn55gIXhosBFIpzJu2xu+nWOcgFaUfexrn8Ys5rd3nMEVyP9C6OUMZGcdIjpnQ\nBKSp5XUjhRQ3eHN03uMxcsp1xOKMGHvGcSDGkZRH/GmAzjM7bAgzz+HhIZqFtC5E3yPmSbGQooF5\nckzEPILr8DMlNELja/x0ja5xjGON5slJKQw0By916ri/BXz24056odDEqUaITsz7GjJ3J0ys/Spb\ns9ZVcZLwGvESCRIJGq/A+wp+99u3t6/a14AP1yz9+ujzWPl+RMuHpZZ9EL8Gc9vDrz22P4E33ATu\n67Zds/VSK5XCteNzt74elXAdUy5Q9jq1MnVguzXKtaNzF0se9gG8ViktXskTK5crZr4H5DXZ+RqI\n90H9lp/iQ+D+oX1XX8U3ba8wznwnn8ikn9f3uGtf6YZWlTyzCvrF6jQ7Z6crHj1a1PC3XCeH9upw\nEqCA4nF5qqlcMsUySTI4RYBcCi4Z5l2dVFiENPWWMkk/IuCCwzUFPy9oVzU1uoxz1MJYY0vcVnno\nzdcfcu/gHoYnl8I4Fs7PLwlN4P69h3Tzlvms48u/8WVQ5YPTJ9x/7TMcLmtNcU3Cw5PXmF9eUMaB\n3/Ntn2UUJQ+J0+0Wf9DwcPEaeXXJw8NHyJHjV/JX2G57HsyOeLzuee/0knc+93n++c9/FxeXp0jb\nYmOimS2ZzZdTh5UpJUPJ5BIpJmiAUjIaWo6Ojxg2G9YXG5wGyAK5SmEuKL6x/5e5d4u1LcvPu37j\nOm9rrb323udWVaeq2477krax3YE4MlGDLQLkARGQEC9EiggKSEgkQkIiSIhLAgGigJKIhxDkByRE\nFBIkFAukJJZoy0Gy46STttPtbqq6qrvrds4+Z599Wbc557jxMOZcl33OqTru6vLxkIbmmGPOvfbe\na831zf/8xvf//vQpEn1CRE9MHm8cQfUID0JHXPK0fkEXrmnblnXneXx9xobfwz1uISw0ZUNV1VRl\nx4JLhNSkKAnO0bcOtKaeloggsGW+kSBD5kmjxHc93gfW6zVt6wGP1J/wG/FDaO4FFkBFzECtUl6P\nGKFyvPByZuMoK0womY2yMpiP3WFEfwDW4+sc8sRybyxIYtzftXQDSW7cDp6pNX9ewtCOatkD8y2o\nDwva2/EI7ukZYJ6BPJeVzGUht08c46uKG10eAneS7MBc7ubSaG9rxHacHRIzoCsdCFoN3jZqy5nL\nkTMfF0H3I/NnbsVHHONpUP8E7aWCeSIXXWD4QPOjZb6oZALIqfgZvwU+paFIhWC56Hjw4VW+Sx4L\n6tJSFQUxJFyfbxPe50VLo0uECIjksj+JVIQQiKknBkEIAZEMosiyOu0kXRfyh1bGLbemjCRqjzQC\nlRSdS2gKlIIf+8wr3L/zBqVNKKHQ1iL7DZvrJe8trjh/8B6mbrh15x59DFwvFsjo+PwXfy9VWLBe\nXtJePEQpgZKCZddz//X7hJRQybD2HUaCEpo/dOvHeVQ+5u0Pz3jtjS+Q+o5mMufNB2d0dwL/2pe/\nQlgvM9+dQOuKJOTwJRKUxhB8SwiOSEQlCC7gQmRaVpRJY2x2Z7S2wHQKjxgStjQxh0qkXhCcx2vJ\nlWiRKWCUxS8v2IQly25FSjnhyneezcIh5PvMZjVNM2W92lBXJWVlgVzCbrjH4J1HSYGyBq0C1hq0\nAaHIXHmK+BDou6x6soXCJ0U9+9SLOn9sU3/xP92N/8BXUH/gn3vqHCN7bHQYekzqMdJh98dpGKce\nI4b9uHdsGBvRgzgE6l0C/j5w741Ha1mxg+pxbHAY3FPuh1uqZA/eR5JlhG6H2QK9IOEHMJfp5s2B\nfHMQO2CX2/GgXBqqKI2JTQGNFzudzPYZIA06GqG2+we3lqgJQhGkIsR8XoyKGOXQb+ynIYM5im2i\n0ViqL8VhwTUNdO+4SDta7+5vd4sEH31s3D4vBjn7Kjz66gtddy8C5s9a9DkB/jrwGXKl8n8TuByO\n/SfAHyeLdv4k8Hee98IpJUYxVozkLK7h0QqRH0UT5DdSDo9PQzgRY+LDDxagMy/np5ba2FxdyHuk\n1JlzjyCRGCmRVYW1lqaoiCmxWF/T+w4ZZS4wrA1eeHwA2UlccvkmYHKpsyjjcJfJNQwlJc4lZs2E\nN159g7vzW5SlQTrJrbu3eYynlwalJc20RhnFo4fnvPraK7j1Gmg5//D7vHZSU0+mXJ89pNKJom44\nf+e73H/9dVbLJT4K1strbp2c0BzV9N2GL772WU7NlIfX55gY0FXFxFT4VmCtY7HsECGvSkRPvnmF\nQFHUpJhvkr3rSAg2XUtVlxhT4FXACI2qJOXEcrSeAi2PVuc4kWudbpxD94IQe5KUNFhW/QYVHFIt\nidrj8IToiQGCz+b/wiWun1zz5PySaTPDSJUtb1cbpNC4PuBd7lpphJQokbBKY6xAm6wsgGyeZpTG\nSdAGZB0RpcbYT5Uzf6F2+h/8ycOJtHjqHBMzEBuGbXK5i3445jAyA7kR/ZZS2Y73jmdOWO6i0b1x\nRG6j1nG8BXGxd17Kx8zgqzJC5ihJHInRhNgSLA69B/Rpa2MbkXh0vhGktAf/I4Cze34Qw3zaG5Of\nSGQcfw48ik5YOgo6Ctph21HQpYIuWjpRHHYKOmHphaWPwxZLHyy9tzhv8c7gvMZ7Q/Ca4BR+8GyJ\n2y6HIhZyW8wiDcq3rTRxlAKNoL0/HgH8edTKR9Esxz+X+9i++V8+97p7ETB/1qLPnwb+LvDngf94\n2P/TwJfIpbS+RK5k/kvA54d/56kmhMh0R0xEYRAxgczWZylmmBcygoSQsvrFJ49SiiQEzkUuHq+Z\nFIaYLKKucqQYgCQJwSNSLmBRlBpIlEVBJUu00QQBcXnNuKDmZYAUqGpDcI4QNFGErN+NBlyOSGNQ\nRJONf7QIFLXi9q0JzdGELrVIjmhEzWYy4+F738ZtrrGFZXo85/5nfoTNasW9z7yGiT1WOTyJsw8f\noG1e+IwhUE+PObtcIFzHxeUFfcg3rSQCrXOUseLkeEbdlDx48D7HdcP19SV3X3kF1yVizHdCWeQs\nT6MN9IEQWlRV5PUJYVguFyA8facxheGaHudabCGo64LJpGTTGsqiItUOv8mOG6HPKh1lFcSED5GF\n92AjSjrQAW0lYZ3XQoJLBCeQoed7b3+fqmgIDqRYIkNis1mCiAiV6zgW1iJR+Ys9yEdDSPlzIuVr\nIEJMgYQnSUlZKpSyL3BJf7ptlq4/9hyDQ28BfODA49542/th32PoD46N432aIQpJkiPNsAfw+8A9\n7G+ph73IXBPy08JedD7G/aM6JCaJRyExB/M7Q1xDj0WlsAXnvRIVBwC+nRcfcR6JgBrAuaDD7kAc\nS0dJh6VNBf14fADzfrwBiIJ+2O99gXMmd2/wbugjqDtN2AL6YJG7D+Qe8AOQj2C+D9jj+CZQv8j+\nJ2gvAubPWvT5V4F/fhj/L8BXyWD+R4C/Rhb7fBd4C/gZ4Fef9cIpZUoCEYbFTQgx+wQCyJQrleTM\nr8yXjzJGISQpRtZXPauZQ0iPjIEUhqyzmGVsiJyEpEXmxQtjmFQNUkuCCHifCKHH9X22BhACoRJF\nBX0n2aRc4qxdpxwtqkBUWdEicFhtiKrFTBRmIghXicZWmInBO7D1e/Tdmhih3XQ8OHvIUdNwfDql\nf/SQ1fKa4vSEJAztZsET9wTvAkkIGh9QViGVyU8aSYDQNFXJBx+8T6ELJtMJGsn14pqT+ZTbJ8c8\nevwY5wNFMyEkgUgKiUZLja2nhG6F7x2r9Yq23WALgxACbQrea694dPGItl/jfIs2iaq2KK0Irid0\nCWTEC49GD9yuQjpJsh6VJEJICJ4QsncN3pBiVp8IFJtFx/vffw9CxGhN9JHV8oqQ3GC8JgjR5QxG\nJEYNtFgccwUEvesRSW3jQlMYhByM1z699teG6/4UeBf4z8jBzkGbxqcj8ZttBGg9gjl+C9CaZ80N\nY9xTcyNXHLfbPUCXe6A9AvresRiH/SFqV4QtkOsDmgVIDJG3IhDxe9F62lsSdRgMbhvVj9H7oZXu\nLhJ/3nEpB8BPkZDULhLfRuiWPuUovU8ZwPsB4HsK+gH0e5HH/TgfLM4ZvN8DdK+HrghB5Sg9DIAe\nZAb10WRuG5nzdNLQfmS+D/LPAvrfJQugd4GHw/jhsA/wKofA/R45Qn+6javnMW1T+kMcFjsZA/SA\n8IagQ66oMixgkBIqJnwMhF5w/WRNchK3ajGVRaGySiP4wRQ/5htFCFhrsVZhC0uyiY13XF30BJfw\nMWLqiBQgC5WLB7ca0WZppEMipUeUEYVGmoC1AWU6fOrouyV3wylet1yfX3F1scFtlngXqZo5pimY\nTQtunzQ8+M7/x2tf+Az92xdcPHnMUTXPj7rKslo8JgKN63nt+DYXj6+pSsv51SVR5KeWJ48e8JM/\n9dO8//77We2jNNYK2nZFCjn5pGs7rK2zUZmyJBQxQtHMIEW87+i7Da53yEZhreX9Dx/w8OxDTKHY\n+DU+OpRQTKopm8LjLjuS1uAkxoBOCtxwE1aCpBLBZ+oshYhwueyeiAKjBGL4G1arlvPzcwppkBK6\nfkXft0QB0uTkLp1y9D0CeZQC8BmcYkTECFGTi0InlPzUl4CeKt78rDZ9Bq2S2/g8DjqNOvEBOIXD\ncLg/WsmOevKbxzKw+xy7yAHAh6pOW9A+mB+OsUe7yIFDH7j2MatUDzH2aBkw8ggj1AbU8N+ILcB7\nFD6NLLo9APMDkN5un54TZGfHQ5CPRNQuImcH2F3aAXuXcvS9i9ztIYgPAO+8HSJyvYvMBzAP2+0Q\nlW+BfKhGNCjlDgD9Jjg/D7CfNX+zf4L2w7j6d4Ta848/3YTIShU5/Kc+DWm3eQEkRTEcCyivkCo/\nUuvB2CYr1LPy5OLRirDRFBNJ3aT8uK0VToCQOYJPQ0TfO0eaJKQRNKZkWZWsFh2bq462zyn/1kYC\nufaoTJG4AYhIoelSpIwSpESpSCRRFZJFd0mF5f7RPRZnjzh7/4zF9YrgHe16Td0UTJoJ09mUR4/f\nozKGx9/5DovNGtE5Tn70VWBDSvD48Rnz47uAZO06jLVUZYk1CltMubp8Qj2dsnEdi+WCGAPTozmE\nQLu4Yr1ckWSB1hYhVDaicg4pIiZqXJ8omwmbboVQgsIUkKCZz/n6//t3+O7195nMakLRoa2g8x0+\neoqipNWe1nuUTBihqKyCBC55epdziJTI2asxCYwqQYTsPaI0KUBZWGpb49cOT49SOaHLOZ8lYSn7\nm4sYiR5SJJeUG4yU0iBUjmG4aYz7Mddifdlt9lww3zWNQ6U9D/C9sdoH7hv9Wcf2wTrKofDwuK9u\ngLzKC3pRDtApxK50XRIDkO5MtLKx106umI8mQO+APan8fcFsI/nxJvC0DcCY0fqsUnQjgD89F4Xa\nAnefDsE6g7jd0i6Hx3fbftjuqBU9ROga7/QW1INTezTLjjPPkXmuGZoCmWrJtcg/vu/ryJ9KLuKl\ngvlD4B7wAHiFvDgK8D7w+t5594e5p9rlB1eAyEqE2mIqm3OChuhckhBBEmSmHESQoCQpBJTKypYY\nIv8k8DwAACAASURBVMRI3Hg2IeBTNmKKwVIU5ASSXK4bABGhW65J0yMkAiUUZaEprGEtBG4ZCcGR\nZpqkBk5YCGKfF2b75EloQiEgSrTQaKHwQtJ3lyT9Ov16zbSe8cRecv7gzeFLElgul7xh7/Phg/eo\nC8H55RMenW34/Be/wOrJFQ8ffpeT+ZzQbbKxWAJdWJbXa6Lr6LusfV1ulkilObp1j4DAu0DnNtT1\nhN4kri8fIawmRlCmZNX1KBIhRmKhUMIihWd5taYsSmKUNJMZnW+pqorvv/Mhb37wXcojQ33LUM8N\nulQ4Jyh1wcONIwiQGowM1DqB0Cy7ljYALpCSAqXQFJlRFZLMeGlcCkihkFHgg0erhHOJtvO51J3J\nfDhe42POCwjJk8gePiiF9xGlJDEJBIHLBy3X318dFCl4me35kfmubYE8+Z2vSdoD7LS/DQfnavzw\n8wGVfLZolSIDtRI5ilTDfpS7+VHFMkbYYqxBups/lCEeCBp3nPmARjGNQdVhFH2gd0mjjHEPvEVE\nphv7N0Bf3dgPqAGw7Y4+GYE73QDuYa4fI/NxnApcsvRDVB4GjnxHseS54HecedhG52Jv8VM8zZl/\nXI83xjznvE/QflAw/1vAHwP+u2H7f+7N/2/A/0CmVz4H/P1nvcD81SkIkVP0Y46ydr7mKUfUKZHC\nkPE3VCeXUmTXvkHSGHwAkRUTLCObFIneE3zCFCZTJjJz7M7HHAX6XFItiYQVBi0kUghc5xBB5IzU\n0uGdgRSIId90tJXY0lA0EWMtRieUyrKlTZ8oS8OtZs5ydcHR0ZTpfM6js8dopdHGsF6vCX5F6zQh\n5IIRb37nLe6e3KauJlxcXjGrimHBJbG+XhOTRyaPcz1Iw/n5FXdv30Zqw/n5EzbdmtksV1Ty3jE5\nnnN19ghVa4QIHM3nrK87los1J/qU6wcfoJSmKjTrLqJVgQue46NjZFnyW997j+V1pOs6QloTpGVe\nnlCVBcJX3Du5y9sP3ifKiLEVQnSUVuGTJoQ+fz4qLxRLEdCUKK3xydMFTyEtWmtC71FCEIKk9x19\nF1BCZBdG5FAfFnoXiGHwxRHZckEi8ueKJiXFnTdqbr1S0cxqtIJv/coHP+Bl/cNps/jxC6Aq7QBc\nDdSESuEApFUKWzA/3B/PDbvIfARvlZNeUhTEKHcgnoYofQvPNxZE05YZBwayc5QMs8OZDNMqfz75\neWt7bDyXUW54A9D3s0f3gVylQ1DfnZO2x0OShyAeR9DegfXBeDi+D+wj0Dtns2rFaYLTA7CrISrf\nB/Ucme8ol8yZ58j8GWAOHw3oL3L8E7QXAfNx0ecWu0Wf/xb434F/h500EeCbw/w3yQ8g/z7PCZWy\nxjxzcQkypRJHf3NB9EOVkr1HaAlILwkqgYh50TRCigkfHD7GfNNLiRQ8MYItsn4UAkIoXAfBJ3zw\nSARGGQqpqcqCsrSsN5Fuk61ZhXRoKZE2RzimltRTjbYKpRNFpdBlRBeC9SaySEu64w6REn3boqsS\nqQV9GyB52m6DCA6fVsyP5iyXTzi2BZOjGYUReJcLUSehWG2uef+DNZPZKZMCrp8sOJrPEYAuCvpu\nw/XFw8HPRlFOJkghuLq8YjKfEESBtiV9F6mbUybT2/huidQWkRy9kyyulsyPj1m3C159xXJN4sni\nMUVZoFV+n3rnuV4tmdeCQlUUtsCgaBeevg1UShCtQ2qPitkMjSTQUmCVxUhLxBCNQnSKvvdZ1w/4\nIPAu4kKLEqCrImftDrawQihKawgx4J1HGpWT52R2ABciuzZenF2ilaGZbFPOXmp7sch8B9AqBeQA\n1HLY34L1/lwMT/2cSrviCVGJnPQSx/EA6Dei731FS5SHx0ZIHiH6cDskGu2XgdtuxeDEsTtfpLSX\n+h+eCd4q7YP4QO+kp4E9Ig8i85H/3s4lSx8L+mTo4wDge4DeR4tLBS4OfLlXNyJw/dTcIZDv1Cwj\nX37AmcOLgffHzX2C9iJg/rxFnz/0nPk/N/SPbYksN4wyS+nEsLiX/zFxeBdIZD8OIiSRo2UvUEnT\nxQ6VJMkHYgutD9AUGJGIUmWb1yEm0UqTosC1DqUMMgoKW3J6JLm4WLK67nAORKEpm4CUCmFzAUFT\nSaRNSB0oKqjKAt0EhJG0mzWb2LNar3GrBWVliQTKqmQ6LYl+g0qBtmspTOL64jHz4xNiiFyePeH4\n1oz5/HUuz7/L8a3bGGk4O3vAnbs1m+U1znU8fvSEk9unXJyfUxclH77/AbfvvIouG9o+QN+iUmDd\necpGYaylnh9x9WTFZhEImw1S9ATXsVhtODm+w+Lykr5f47nHd978FnZWUAiJ1IkYNckn2n7NUidq\ndUpZCKwqWW86Fmee6r7Ahm5YDA2EFLFFSREVlbTg8/WO0EgZ0UoRXcJ3Pa5PdJseZKKeWmLKNxBl\nbabQJBRGo2Si144+xHxTFybXGQ0RrTWnt0/QOqBkgRAv3wJ3BPNRejyO2duXcQfGh+O4A+oYDoBc\nHgD67txMrYzgPWQyxjEi36dVdhH5doE07cD+cJlyP5s0y1ETQwLNTVIl3VziHP0f0w6gb4C62gJ5\n2IF3ymZi+9ml43gL5sLu6BMOwXofzPPY0keTx3EP1J3JvPjIjTtFdDtQD1tFy6BkOdCX7xZAtzrz\n8YN9HmDz2zj2CdrLywCV2egqZJxFAklJpMhceH70yxmhWZImCD4iNTmpaGDxfPTkstyKFBOui5go\n8Sabz8syYEWW9Skt0FoRgsN7i+sDxlq0UQgqjmZHLB6d4VJCFgJbWmKM2aRKKuRgoUuSCK2QtURb\nQZI9WhiOqFgul7TXV7g+UBYFvhRIOopJiRAOH9ZIJSl0jXMdt+68RhKB2eQWq/YCpWuqZgrGMOtX\ndD7Sx0BEsLi64PjOXc7e/z6nd17FaI22NfV0zmqzwMfsh6KU4frqgnW7Zk6J1oIuetarFSH1CNfT\n+5xs0qVAM59ibMXZk2vqkqzrluRMz5gXode+ZyOvIWhmhWW5MFyuHfXCoBVYrTEm12nVPvvp+CQR\nkZwF6iIhhvxZxYDrezoXs1xRJoTKi7AxZcDOstWA1gKtZX7vnaJ3ibHqgFa5IpI2BlsUQ7T+Uish\nAjB9AZ35FozjsMgYw3asYj6WQX48FrdgLrfjfCxpsVVZxDAAuX4GRz6OZQb9LBcdovi9pceAIqbR\neWWoOIUipnEBdA+Wk9rbV9sF0Qzm5L/zAJj3wFqELYDvTLv2AT9sAT8lmQGZZwD5XndxAPYwAvxh\nd9Hi3B6F4nLkHfwO0KOXW6CPN1Qt2wXQ7B5xuADKx4w/7vhL4sw/cYvkL6UQaoi2RzVLplm2Frgp\nm2uloUBBjAIE+OhJyJy9idg6sSk0yUHoPcEaYp+140nkC1SpbHfbtWHQPZfEkIHa2oJmpll2Lhdk\nyGbr+N4TZfZxCV4SRUT2CaU0thQgJrTLC76zepvX6jtYFNoE6kowq07wrmO5uiQGqKqKqpzQTKY0\n9YzLq3NOT2/hXK5vutgskWqCtg31ZELoV6QEXd8ii5qrJ5cILZjdOuXy4ohmOufhw3OaCjabJUbX\nbNoORaBsTvBuyWYZuL5a0fc9sc3KFFNXJBWo5lOKpsBMJlymd3nl7l0Wy3O8CyAkIgqCByd7NvqK\nqpwyO1FcrQQRjQuSPmlUr9CVIIqU10FCoPMd0msIEhf9VhseXEAkMEKx8husVUPWblbGeO9zKToS\nRSGQylMUAtqENgUpBYxR+C4hpcEYhZCR3gnM7wI1y4vozDMQZzCWzxirp47dPL4bZxAfAHpQpmx5\n8jFS3kbjA/c7APrNyHwrMSS//35UraQsItiXJvo0ChiHJdq0c2YZpYs7V8Wdj0umV8IW0HNy0Q7U\n920ExnFE4hiyOMUO0F0yh1TKHpC7aHagHvbA3GuiU0QnBypF7pKDRmAPkhjGY4OiJYw3TXZR+bNo\nFp6zfZFjn6C9ZKMtgUh5DV0MqodcqGKQnA3mWlsnxe0NQCKHZJ7cJChIDnyMICMqWlrnUV1Bl0Cb\nTLHopAhekKLPHi5xk3XrgEZQViV9DKQYcS4hokDE/AXwElzMafFJWDabSFGV+akhWS7jkrbdgBBM\ny4qqqOn7BSkYYoq0mw3z6YzVcklTV8jUMZ0dcXpyF1lPaC/PKHRFSoGjacN13wAB59ZcXl7yhS/8\nFG+9+U/4p3//H6S0mtdevc+mbzHS8+T8Els3rNorgosU9RGX19ccm4aiqtGFo5nNePDWO+i6JKWE\nD4FKC6Z1wwfB87UPv46oDRN9ymrZ4vs1SkWQOTV76ToaWzKpBbOZ5nwR6QL5SYZEhSbFQCAQO49M\njtCFLI2MgUREiUQK2egspcFQCoFLDklCKUsUgSAcRheUjaYoBFpbqo0i+Gx7nFJOKGqaGh8CkC16\npXz53iwvkgGao/Ghh4iIKYN1SDfmd+On5oe5pAXRDPz4ll4ZQH1vkXObTCRvcOlpF7UHNHIA8pGc\nTOP3kz1NedoXSRpcGlXywzaZgWbJ0K+3KUV7IM5AGe2D+Hb/EOwjEifMDsSHyNxhDiPyaLaA7sJA\ns4zjkAHeOUN0MvuW73e/15063PdjZC620sRncub722fNfdz2E7SX+kyaq6pknlQLATJAHLI7Q9ou\ndiU8UgzRBmN1IIDBYU0ltIEkE77NGnWlBUJE2o3PNrpRUVbZzVCGgJKaruvw/YqiKEm+x/sx8SQv\n5tAKnA94l1U0MQS8yFGnkJ7VpccWES0kOEttpyijaYoSKxTO9RhtaF2LkpLpZELfe+7duU8Mkfnx\nKdPphEdnD/mpH/08b12eZ6fGYsKkabh6HJGyRqkNVTOnrhtO7rzK0fEJwXVoZSkKxfnyQ2bH97i+\nPCemiNEV6/Wa+UlNiJHgVlS1xRqNtJariwuOTuZ0qxXN8YTju/f4tSff5YolZVmxSQFpNAoLqYMo\n0KKgjAJtHUWlmM4VV30u8tF1CWMNzkWMkgQXCFHgeo9bRdpNIKbst14UxUCjeHyIGCNBZMWRMhJb\nZz8WZQyzpqCoPZNGItC5cIXPpfNSTFhboERe3C5sQVEaQnAv8YrO7UUWQGVMO6COCTEAswhpD6yf\nP7+9AYSYo8WbvHc6TOnfRuWj5lyJwVhqBPP8s55cIB32EoKSQuzPpZ3BlsNkuR8DsA7RsiM7Rz7P\nWXHbxa6wxSGQH56T4gDmYgBy9oA8Wfo0/B0jmA+RuRt9WKLJ22DxTm+lhjuQzvLDnaZ8J0d8Fm+O\nfwZnvr991txHHbs5/gHay/MzT2mIrkBqgd+ueyaiFKihZIqQmX6IMX+gYnDf8gMrJ2RAaY0ygUJl\nbtYKgykiUgX6jdiuVbgYKJOFIDJYCUO3bgmdoO3WdJsVQkREBNcLQvLQ5d8XtcgyxShyav5FYjHx\naBUoCrCioqprmukpssuSQlMo/LrHO09VVdTVlN55qqZkOplyND+hqics255v/OZvUlWKZbdmPjki\nxEBSFUUhuL5wtN2G1WbFa699Bl2UrFdP6HtPGzec3r1Pt74mSk30JQ5HVVbEEOnaNSTByb17nL1z\nznJ9hdGZs/Y6sDw/Z/KHv8B77/w9ZMqValOQSCnQFoQyED2lMAgCRimUXCO1QOiIV4pWCGwK6B68\nEVntEMG3Cdd7iImQIkYqRBBoKcFYlILgPEklJpOaujFUpcAaibUWrSRahyHtuycGRe97lBCkmAuM\nMKyxCJmtZMP22/Xy2jR9fLUjGTMYizAAdUy5JmVIGaT9cNzfOB4T0g8/FxPCp+zFfQOUd9SK3CUQ\n+b2korjHm2/VLoIeO2D5Pu0Shyj9aaMtl8wgGRx13juVCbAD5HRYkUhtbwfPAfYb50SRwdyJvZvG\nHs3i0gjieeuCxQVDH8wwzvpyFyze62x/6yXR7RKBottF3mMUnkFcHCQNPZXOn9+uXfsk40/QXh6Y\nB0EiIqTcVlRJMRMuW9dEBNqInCASBGHI+08CclqRpygMpoo5TVBqunVEKT/YqgralcsfSJIEF1FK\noI0mpsB0OsFIxYcPzoltP5hqRXwX8MvhMZSIIlfmnk40qzayWSh8hNVZQsSOZma4NZ/xE7c+hw49\n63bBUVOjpcIlMEajtcWHXLNSlUdUs2NcSLz7rW9w53TOSim+/s1f5/bpPVRR4SKUdUV0gcura964\n/2MEn9U1PgRIJaoInNhjgrVcXTyBviWknqqcEZNntXiCKRzz01uEbs3F+UPaxYLJ8Zx+s4LJnNOT\nU2RzTPKK6/WSTbvBeYcPHiGhjZ4YPHUsscmiRfYvlzLLApUGgaPz5Ci7MwgM3gWCz74wkP1qtDSk\nFCFCVRhi8CRtUI2iqi1NbdEmZV96JMILYq/woqewJUYqVq3DC0WMASfWWKNJybHpEioafmjfjGe3\n18mGc3fI2PZXgb9886QX0Zlnr+7BRGwAZRH2ur+x/YhxvEmtjAueI5jvpfbHMGrQb/LlOwUKsOPF\nB+56G60PypXMkZttlNxR0qWCljK7GqYS2IG5HuqV6mcA+b7d7n4d0QMwlyKDNDtAd2kPzEcQjwa3\nBfDsu5IjcnOQxp+c2AL6bry3PeijwZbYbgkiL37ejB1uXn6/3f1P0F4amAvIQC5H7lSCyHdgISJB\nCIyGyWywpXQRpTqElMQILibQimoimcwU16uAELnCe/KSogQvIuJaZb47RgQObRxCWrQumE8q/LTi\nerXifLPEWEjOoaIgulzIQolM10xmiuYIpiczLj6MLC/XuOtArzUywq2TiujA6RYtBVJY2uWStu3Q\nuqCsKrwPiKRoF1e8/fiM777zPd7+3rvoJPi9P/F5MFO6WOGMwW06YgpURnF8PCUSOb11h8n8FKUk\nLlmulw957Y07lGXNpX6XTjmOJ3Ni0KzbVY6ijaVvHa6/onVr6iOFNhXNkaQ6OmJ+OoXQ8+DBIy7O\nL1heL3Bdh1GCVA6GV07hfE+pLTIJhLQYK6CQqABJC4IPWU8u8hMXnvx4LgVSDIubQqJVvlGXlUUm\nhSkNfQoQPWEjmBRHSJFfrwsglcJLhUmJbu25WgaWXYvre5RSKJ3NvpSCuimpyk/1knbAfwj8Y2AC\n/EOye+hv7Z/0IjSLiNlEipgQIe8TUi7uERL4EbDZG+f3dTfO5488+ehZHrccudim8Ec/qFaC3C2W\nxt1i6RiZ5xBrR6OotLO5ZbxJ7MHsGCWPQL6hok0lLSU5rWgEZv/RW5G3NysV5RuAz3LivcjcMW53\noO62oD5G53vA7s3Qc8LQFrhdBuURuBmUKgf7g1PiSK/s5Ik8DebbD/gF5z5q/rfZXiLNMsgREdlU\ni5iBYLhohAg0zZRmkkuCrZYbjFdEEkLmxVLX9zQnmqqJLNsOIROm0LgAQgWUDJlzH2hApSTIDoRm\nOp0zm02pqwKjBL8RF/RpgfCJMmWjLpckSQaUkRy9rpmfSMJa0y88wSsKo5iWx0gZSb3mev2YubxF\nrQuuri5w6xVFUeQ1AO+wWtF7wZOrjrPrjr//rTN8qnj/0UPeefQb/Ms//zM8ulrQLE4oK8FpM6fr\nr5AIpk2D0AZbVGAMy/YJHkk5nbI8f4RVcHL7NWISOB+pZSIE6NfXpNBilOXOq7dJ/W0ePnyCrI45\nOp2z9pD6S1rXsbpYsllsiNFjJsVwkUWSSLQBKjqkbhBeonRCa4kqBKIQyCTwwSFRe+LqQYmkJMoI\nwCOEIiWHVJFCC8oS8BHnQUo1uGaWdOtEu/F0G8982hPlmmUruXjSs2pzRqhVhuA7Hl9eEULP0bxk\nMm0+zcv2wdABlmQQf5UfBMxT9tonjkA+gPgWvPe3z5rbAXtMh4qVg4VONWR+Dlr0DORyLzoXu59P\nebF0jJEdfptWL2CbFDTKD7dgPnihtJRsUsWG3HdgflhObgviYh/QPVrswH1razCCuRjAfATxNCy6\nbimWvch8iM79XnQ+ArlzhuAVuFFeKEiOrc/Kdu7GdgR8wgjqu+0LgfEP65yPaC8VzGPMHPlYhUIg\nt2CtpKRsHPW0wgXJupfYlAYFixx8JTxFI1GVQ5Qg+4QpwXc5WrTGUM0U/dINlYQSqIAPGxKRZlIz\nm84o64qz1UM+OL/CSokgMjnJPiAhGoJIzO4KJqWgTWAnihLB6cldmqIk6YSzWQK58QHJhtXikuQ2\n+FDR9QuKwVe9md7h5N6X+aV/8Lf5zE/8M/yzX/kX+Ct/9RdYXD/iF3/pV/kj//q/xP37P8qTh29z\nFHtC75BJUpSWsqooJ1N6r9gEwd1XX+fiekPVHHFSzwluzWaxxF88QqIwlYTUYlSiqDS3ZxO+/eYH\nlJXl5HRKYRNNo0AVvPW9d1g8viZ5wa3bJ5hSsRHXCJHwUhKjI4iGJKAsgD4vMqNzIpCSAuGGBbXO\n4/rsfClSQmsBQuTFUR9wTrBpV9RzTSAghSaFgEgS3WuMtKSo6FjRrQKtaWmqAqlaFB7fJkCC7ihU\nxb2juyw2HZcXT1i369+pS/izwJeBX7t54EVolhHIx+hujLq33d0c7wH7jWMxjhHzwHPvlUmL46Ln\n6M+i5U6TnnaLoGNkngZFi8MNzoeHfuYRQRj8zB2aPpnsJU7BJlWsqXNPNQmBFv4A0LfALvbmxB7Y\nC39j/gaYi7F8Ru5+4MpHQPd7NIvfA/Jsd5sj9ODU7n10u/dxH9Sf6sPnlMbjNznzF22fIgv4EsFc\nIJBZjShEVpwMSUIpJYzVFHWknCaMg8WVQpEIIVMyUWS6RemsST6aNawvHCpFbBEpa4nRgnQKG6lI\nMVAWecFsvVpyJVe88RqUjcWUmrt377GO7xG6nvKOoW4EwlqW64SyFfVkSYweWStEs6IqFfVUc3o0\noxM9Ia6HN1TSdx0SQY9itV7hfc9m09FMSz73uZ/k7XPN8f1X+LVf/xp2dpdvvvUmKq15dX6M6DVC\naSSgdF74ffX+PermiHrSIJtb0Dk+/8Uv8PZvfYMP3/1NhIL7r3+Gu3deY3qkKa3l4vIx3nUYpTHG\nMD0+obYTvvRPzXj4/gOqSkEKNNMTHn3vbfrFiiN9RFSBiW04ns95fxNIcZXVLNFibEKlrDRROmKs\nQBUJREBFSIOdQiQNiVggh6cckTxWZ4vU5ATtpiceZ+gA2Kw6pBeoWjIpSirT4KPnsl/R9S1abwix\nBxKbVcTqI2bNjFJZ6mLKjxyXrGLLW2dv/U5cvhPgbwJ/ihyhH7T/+hd2ipqf+3LuT7X4ET1wAPQH\nY7+3HcYi5AU7ocXAw+dkoFx9BYYKx6QkcwSVJDFxo8gysJUg7lwSx7aftp/ELhN03/twvwfUnqSR\nbfk3QcoB3PhqYu93CZ46J8+n4YlhUJ4P3/+03YqndNxCpCxmEEN26VAUW+VK2tuei0ELkEPuody9\n1nZfjueKzApISOP8zbqd6anB4e5Nc5P0rMn99lXglz/i+K69NDA3Nmf7KSEIxJ2efEgOMqVAmYQ0\nPQiDGkyugnODtE0iqLE2YSw0U81qETEyQJNQQjCpNUhJwiF9Q2EMWkDbOx6+f8Zn3rjL8a0TAKqJ\nwRSKRjcIYWmOCspqSrnYZNN+2yI7uFRrZBkptKKoJKU2YB2qN3RiQ+c3iNWGEDxaKpb9NUTF0fEp\nX/jiT3B05w2+94//AbePjzl78pj/6a/8z8TY84u/+Lf4C//Vn+PBk3OsEpmW6A2FLfCup6hqRFlQ\nNlN82PCr/89XefPbD3l0tUC4DW+/ecbv+/1P+OKXfhLne6QQKCT19IQkPDIKOn+NLmte+8w9+j47\nM1bHUx49+IA37tznfPVdYgqU2lJXmioUzNyEleiQaU2igGSzVpyUeU5hAIUUkAiECEZZpApokasx\npagQIlFUiRQCnfeI5PFeomRWD/UrT7daU9kGWQqUDNSFIYiCPnWkaAbDpp7oA0ZalOiZHp1wVN0i\nhsREzVE68Q/51qd66QL/B/C/sjOYO2j/xR+7MfGs6O2jwDzd2D5rLj09zoWuxV7tSrnbslscjUO8\nnZIk7GnMRxDeqbzlFrBHL8UDJCTfEbY3BbHnkjgUqd430JLEPVCPTwH8Pqhv283xIJYYAVrJMPi0\nDwCr8tOgjLmohUxxq6TJy7mOkNQNTpyDhc20pVT2Fj2HRKGbfHmKW07xBmA/Y39v8xSIPxfUvzL0\nsf2ZZ5yT20sD8zuvNDx+vCZlroWcip2jcq0SZRUxRmFtIplIXStEkqi6xvtcjWa5ECjdYW2RZXNW\nEnpJVWuUCngpKCqFKRSFrNDaAj2xiyzOOs4ePeH0zjUgkQKaaoJ1FiUqZrXF1IKQEm3fY4QkKY0y\nkaJyiEpidAQTsZVgUpZE72ldPxghgQ8h84ZKoYuSajJjPp8jZWS18nSbkJU1UVLVNfPTu5jSoJPE\ndx2xnFJXE7QRNLMTbDPHK03oHf/kW+d8Z91z9/6PkmLLm98/Y/btSyazB8yaAlvWJGuynWyErr9m\nZk+RUtP3PYLAZD7HVBN++Wu/wgcP3uf27Bbn1w/RVlJYxaQsWQeDCp5CNsgYsUpSlzXHDh4tFyjv\nsjxQSELIGbhKS5oyoZWnax3RFTktH4dWCe8TRml88CgvSDGijSD6irbXpCTwRJRS1LrAcIw0l9BH\naqup6kRTCJTdsOkecmt+G+1KRGE5FbNP87IVwC+QjeT+4nPPehG3r48C84/rN4E8DnkRY/Fh2BYi\njux48UimVEKSBwA+gvt+Qv1hpL0fke+9E3tyYrlXgHkEdOAQyDmsKjQCeX65w+3Y9k2/xt819hxp\niyFKFqDEUJlokHemuPW20YQtGx+SJGlJMoMMMYihvucA3mZfijgcC6NkUUCQO848Dn/l+Jnsj8c3\nK+0De3oGkKft+7mbF88ZP7/dfEj4HWt379zhzp0ZWutcI5lESlnPOpkW3DptsNbkauwmUk0SRRUp\nmkg9tUwmEyYzQ1VN0SZzsspAEpp6ZjCFQQmN1YKj4wJdeXSRXQBdm1A6cvlow9nZIy6vHrFewL+w\nOwAAIABJREFUL6nthLIwFHaKFhO0KYAOKSRSGZQyFMLSNAVHVYOSElSP0ZBEYmYaCiWx5IzVdnWF\nxNBMKqZNAb4nug2f++wbiCh5/dVjCA6RPP/9f/NneevNb6NR6EIym8wQOuBjj9Il0ijU0QwRNd/6\nze/yzvWGP/pv/wm+/c4Vf/1v/t/c/ZFXeLer0PYeKalsQiVNXszUJVbXOO+wUmG1YFLXzI+Ouby8\n5r0Hb3Pv1h2a0jCfz2jdOa1fUhuBlYapLpCip2GKNYa6KXjl3gk//tnPMZ/eBhSESG0qGjtjNp0x\naSqmtaUqisyFp4CSkbIsuTOfMbFFjsr1mmxINqGpb+McXG88IUWEcWiTqKuKpsyl/nQVOZpAdSSp\nKlC2552H30DYSIgbjP1U45M/CPxR4OeBfzT0P/zUWZ8EqD8OuJ8XrQ+Red6OoD5G53KnWhmhNe0n\nyx9G5AcuKmIfgvfhdwyaDyPr/Uh8NM3adrEH6PtR+XM5iN1v2e0NS7AiImVEyix0UNKjpUNLj1YO\no3qs7ih0T6E7Ct1S6pZSb6hMS2k2FKalMB2Fyeda02OMw5isetPGo01AGY80AWki0iSEiQiT8jPa\nQU85PNZp19XYc2b6QReRQcrEIacWP2L8/PbSIvOjeU1TVaT0Pc4eLrKcDYGxinoSKZuAtZnzEtJR\nVZrNylGWmr53WGWYqoqy8mht6NoWgUFKR91YtNmgdUKpmsJKtBSk2NF3LTEmcgm1rENPAXq3QhiN\n1QpEgZYlJjgUDiMjjanpYo/AUxQNk2pKaDOPLHWHRCOjovWO1LXE3hOTZFJPUMawXrd88OHbeF3y\ne1495W//ygN+9md+H2X5De6/fp+mOeZrv/517tz6OfzGo5XOEsTFhuru68QIPhms0Pzyr32du6+8\nwh//d/8UXdtSGMPX/tFv8LM/+/N4fRvhL+hWV7SxYz4/BSkI3hH6Hu8btLIIGale/zH81RXTosLY\ngnLZY+OUx/0CIVaURmY6SVmkqCmkZFJaqolB2QlFM2V6NOHs/JhHDx9RKcm0OaKZGoRcQ1BEf0l3\nDSCJdFTFFB1rIhOk3GBMJMVAMzEIX3B8dMLGtSx9R2WhLBNagxl8X3xS3LpboITCGoGkIgg4u3qP\nk6M5IWw+zcv27/EiAdAPIzJPH7H/LIolil1kvvVo2UXnW5pl7Iw1O+UNUy1FEGovej8E8lxwYABY\nsc+7p63z4QjcaZA0fBTVwt6NQAyveRPUx8g8/7IhKpdxqEaWP5Fs1pajcYUazL4UIT/j5bFQRDHc\npA5qe459Z6qVi1LkfRFk1vNvFz5lrnIVEykNT0VxjMiHu2p8znjc34Xyu7vwJ2wvDcznkwm+gHuv\n3GLd9SwvIglHSj1FVVBVAmsVQYKQAVsHuk7gfEsia9LrylKVUFpN8hVP9IKoDbaQKFUgLRgEpalp\nU8cmLIm4rDONLffvvUKZDDE6ohdURfbOFnENaYJULXWpaB2kCEIYBFDpmqOyJsoKW4BUEh82LLtr\nSq+RmyVKGoIEKT0yges6WjwP07cJ5YQ/8W/8i/yN/+vv8uNf+jEWj6959903+Y/+vX8LHT3L60eU\nBoRWNEczhFDYokTZgg9/612++rWv89Nf/mn85pKYNH/5L/2P/KW/8OfpXc/1eoXxSz54+xuoomC1\nWvPqa59ltbzk6OgWq9UTbt95NWv8jSb0G96YnfDOxQPuHN+hW60oKZBKoJWgMImiKCmszokuytNM\nKqrJKdNYUK+PwTRMiprQbTBKMZlCpIBYsFpYOufp1j1z65nNSpyuOZm9RhvnePV9LsM1dTPHbwSz\naU0tLO89fpe4EdjjTGMVlSLJgKSg0jWCKSlpZGxQQiFjS0od6XeBBe4Lg/l+wPWigP4RoJ4iA7iI\nXXQ+2uAegPPOGXEH6oe+hQclIoTc8u5PQY7IAHXAfaex5LPcgvhNbny3P1Iru4XXjxJ8bF9DxFxV\nUrAD85SyDDNFlApP00VCZeEEcueaaAYzLa0IWhGNJASF9IoQYj4nqJ3aaFS0jDLSg89qD6xl2u2L\ncX6PYol7j1PiGdTLD9BeGphnwE3UjaaeaPAdi0VCqoQtE7boKUrBYu0otEWILtuq+gLnOoLznJzO\n0MYyaY6QSG7Nb3H2+AprQWuTZXNUGF0SUmDjPLYKmEIyPznlaD4lhYAXgVJn7w/nIiH2uLCmwOeM\n0eRJvUQKizWSwhhKXZBkTVk2RPGI3q1ZRUelC4qiZLPeYOsJMQgcPc45VqsrPjudcasueOut3+Bf\n+cqXMVLS+YRrr2k3nvXGsbh6TK8FalrRNBVOJGTnKc/POHtyiVI109mMqioJi5b//M/8WV6/c5Qz\nNSvNG3c/R3f5PmePHnC9XFE+eUxZViyXlzTTGSFGJmUFXY9cX9FMGpoWHnSPUFpyVNZ4taKZNJzI\n/IVTWqKJIFo8Fc1RCXqOvlzTB0MhGkKvESlSlIEQl/hNRMuICOB8wFiBVD13734WjST1FZPJbfp+\nRYgXVJOKkJaoQjKdSDZumSFBe6TsaCYKgUCpnhRbpJjnaEnl8n4IR2k/1QzQF2svAubPAvKb4L0P\n2M8C8WFuG9gNUeI+kO+SgvbpltHidqzfubfd0i4DkG+3exC8x++OYhCRdjSL2gPwmzTL4f4+Z/60\nam//39wKVQQ7IM++fLlO7wDiuSi6OPzbt4U4BgVMkngzeJYHPWx3XYb/n7o3ibUtS++8fqvb3Wlu\n99p40WWkM9OZttO98aCwXaY8KFMqBkggJAQCZgxgSNUMJggYMGXCBJUEooRUgFQITCFlUaaqMJCU\n0850pp2R0b8XL957tzndblbzMVj7NPe+F5mRkRUV5JKW1m7OOffce87972//v//3/ywxJFQ0xCDX\nZIkSFSqMbSyfA/PtHMF6C+jqALy3xzk4J4e/6acfnxuYaxXwKlKURdY6R42QS7z1WFSirUGbEm1A\nqUA9Kdgse4Yh0kVNPV0zPzW4qmKmG26fQkhQNwPabVBpjtMTUspWqU1T4/2G+Znw+ksPKMrcQXy9\nGWjqGZEOpXrazRJna4bQARGrhGgErS1lmVuuiRacdTRugjctaRVxqaT3EastSRdMjucYShwe9IZN\nv2GxeEYzm/PFsynKRox1fO1Xf5nv/skfkx69x8On73M6fx1tGqrC0K575lXPELJhmLOaX/u5n2G1\nWfPrv/R1/tdv/CHJL3jplV/EILz68n2a2YYHX3iDdb9GRLFeL3IysamwtmAYetTZKSn2+NWC+fyY\nW/1tdDVwFQeehAFpjpk3d5gdw5OrDxBxDKsFRRmy10c5ZT6bkui4hUOLo9u0DK1HiDgXkUFR2ILK\npXyHrJaIXpPMR8AD6nqKci1NU7FZ9VjXExC0F46nBXptgYGkCvRYdaqmnqEIpEEjKRBJBAkYVaPo\nifK5pYH24yehWW4C9486d40n57mIfMebHyQ793Mfgd/kzm8mQNMu+TnC7kix5CMj9UGmWbYg/iKa\nZU/6PD93L3htbH/eKFdUkpVTKo2MT5Ytap0Qc3jRGbeV2oH5djuhc2s4awk2EK0lurHTkLPZdmPr\nk3NDYy5bGehopfAcgN+cSkZO/ACwd8fHc+oQ1D/9+NzA3MeWrt+gVKRuIPQKiYaYAsYqtGGMgi1a\nRyQpikIzWJV5VmtI4nNvTHLXnzt3XmbanBL1O6AMWk+x+gRPy7prKVyibgyzVyxHsyYXobiCoW1p\nijnGaNrhKX26YggNy4XHlC1xMBhToY2gfHaw0BIpCo1zFVqd4MqCk9M7lCnhl2uaaUBE4eqKxeWC\n2K6ZHx9hixnPrp5RFxWl2jCZNLz9J/8X3eUz2m7BpFZcPP2Q+v5dnn64pplMURfP0MbSPzrH2Dv8\n63/9N/jv/ud/xOyrX+H2vWPOzu5itOaNcsLtr9zHv///Ero1x8enPL1a07U9s2OLtjVKG4w1mGKO\nPPsQm4Q7usHNzpDlE2IRGXTEzu5xNLuNsxXWljz+6E2MLlivLonREZOlDwNNXTO0G05PCi5jAWEJ\nacDoDq3WlFWinhri0qJMQhjw/inW1SR64vCMybSiKU7oWjB2IKUBVxQc1RV9bDGpoS6mlNUZEp+g\nyxavsme9LRUiCWsCJDd6wXzO458WZ/6jQP25uaVYbkTnL+LLJfPi15OfN2mWQ3ni9VQlMGLsTdpk\nr2YR1AvULC+QJe4oFrgWnY7h+HMM+hbfddzTSiiQcf9AfUNkx/Vv9egZzB0hWkywhBiJMaKDIxx4\n32z1/Fvpog4aiXpvp7CLzOWANpNMwWwB+kV82I5eGYH82rlPPz43MN+0V6z7BcoY6lqIg6O0EWdP\nKaoWbTvECEVlITUkQ+6HqB2usGgpKCxjm7msU6/KmombE03BcvgzmvIeUNENkc0wIGKo6khpGorp\nOX1vaDdPUaIodAVW5QIY7dB6dE0MQu97KlXibEFRKJwDTMIZmy0IVM/D4TssVle8ZO5igyf4SFEU\n9F1LiBqlHY8fP+P27cBUnbDpBq5WK67WG0K3gmToNj2XG4+Rlru37mDqCqUMw9AxbHourzbcvr2h\nqO/zr/3VX+LtD1Z8c7VArTrunk35vb/2m/DkHT5687ssVmuMq5jNHYX1XF2dM5lUaDNFG4ccnyAP\nn5FioLJC7Wrq5hirLpmqE1ZywdH0axhtmE3PuHr6GJynHWBolwxE7LBGYsSkK9pBYbWjKmas2g8y\nmFtN2VhOz0qiXBBCwvvIMjyjVZqqqrAO6qYimYizc1IUjDlj8E+oqmOkv8Ka/A9raCitpx08Rits\nNUFiQUwdKQSqckboPwmSfsbjk1QF/rhqlsP/9+cep3Z0y3NAvqVXDlQs++TmDzOnvZkAHYtz5Aao\nb0FZDmJ/tefMn3sl9Rxps+POD1Kd2xc/IGHGI7skqNrx5WzvGGT3NLZ3D7t5kCxNSuNDIESbK0Oj\ny43CQxpdLFO2SxgBe98uTpOSRkchbQFc2AtR9AjwW1oljmAdb3x4IlnNIuO++ikH877vSXQQE85a\nyspgqwKrC6oqURkwqccyJYhCbCDZJbYYiNHhtKFusn+ID2sG33J7/ktYSormFvHqPtY0aFXgk8K6\nSBharANtPKIHWv+Irl9j7Rxtb2NshS0idaNIukOLpRsUvhesFo7mJXoI1KWlD5cM4iBZlF2jlKYs\nStZ9S5EChIDvB4ahY7G6wBUlD+7dY7G4YrF4l/Vqw6uvv46xBc3JLfrNirj0GG04mp9QT+9CCgQx\nbK4uWS7OqadTuuUlpw/ucevLv4Tob/Ly/a9RzY64deeE1eW7/PEf/xGkSDOf0fuAcwY7NVwu1zRN\nAxI5ufcqlDVaFKXVDAmauuGOgpQSlxislPSx5XRym7Ka8stf/R2+9+Y/YGme0Q2P6JdfQB/NcKIJ\noUalDoeFdMRJY1lu3gTd4rRjMhNEjlmuB6IHLx6rNmht0EZDspRlQUgNMSm0iUzUyyjdYao7FC6S\nJDKENW2/JiahcBOIDqVKVFWyWXeI7rGm/ry+0vvxTyMy/2EJ0RdE5YeJz73e/IaqRa5TLbu2b/IC\nIFfbBOkNVbhS1zBnz2Vvteb7nwDcULAcAvmebtlGp3vIvhmLH+Bzls/sVpSg9MF7UdsLA2MEPz5+\n/BsqDQmFcXEs90+5yUdMYzQuN5KdmuT0aFC2fVzaPy4xXiQOgFwxAvgLrsKyvepu6ZWbV+iDIqTd\nb3XtKvWx4/Mr5x8/HasNaKFuDI4SZwyuLNASkFSjtBrNmRxWg7HgisS0tMxKl02g+mfU7oz18Jij\n+lVQgrMFvW8xZSCZFldkT+MkgURLYGA6O0NUj/dXOANJD8yaM5I8RlMyxCE3eFUepXSOtk1O0hWF\nJnJF3x9jU0vDlHl5hNUGmzzL5YeErmdxuWHwA1LDo3fezVRO13K12HB8esIXXn6Jo+MZH30UqOo5\nPZ7TO6/QHJ0R+wFbaKIMyOoSrR1adUycYfP+m5wdlfjOc37+Ft//4DsoSRTNhIuP3sE5TRSHa0qe\nfPQURDh//CGv/cxXUKen0C5JIoSuQ7ynLBxTDEd6Qh8UrQtcLt6jaWrO7Cn65B4P7v8CTxcDbXqP\nYXVOURaIKoipxq8vYKgQAWcq5sWrXHVvk8SjdcWkEUin9KHH+w2u0SQP0SaGocXoI0R1aDVh6AO3\nTqd0myH72ocNooRBhJQCEmuKagZGSEFhU4PSHcvVE+b1K5/XV3o/PgswvxmZvxDQeQ7Idw2YbyRA\no5hd/84bPYC4pmS5IU3cJ0APSvUPKBbFnmbZyRUPXm3rIHP9OXvN+g64dri1P3Pt5ynJCpbDn89+\nHzLIZ3uD7cz7gsaEiLcxa8bHaHzrUqlu8ONb6aKOZgf6e5pFduX+HFxg9jTL9sM5APKURorlANB3\noP7px+cG5qUrsczRJpJSIiqfKQyrUNqSKBAfiCKgHKbsQVcY3SOmxLlsYyt6hbBhiOB7TeUmmKAJ\nKTL0ayAR0kBZzFDk/pNBeqytKVTBydkdPnz65yS1ABqsqXCFIg4dgsEoS9IVWkW8jyircM6htB8b\nQ7fZkncI9DZ7coh42k1Pv16x2fQkbbBKszEWq0DXFlGGYnqLVMx5+/GKl1/+Emse8u1/9E+4fPwm\nj8qaV9/4eVxzRDU9pqhPqCvQTGhX50y+8LOcf/sdEpHYdjit+fDho1Gnf4wyBdK3OCbcOj1iCB6S\n0EznpH6DaZeQoK4rVkswEpg6R/AeUSUqKj6KHU/Pv8XZ5A5ON9TVhFN1m2ebNX7zlK6Y45Qn+EA0\nicXmPSb2Fko7RAJGJkRWWbU0neH7FZtuQKuGwtZolf3NV+tLtJ4icUJqNVoMw6bHOUtMhughhIGk\nI2I3aFXkxt7R4NOafmhR2qP1mmX/F5/XV3o/flIwfxFgvwjQn3vOdb58F4nLi5KfN/nybcH7QTn/\nqPw4BPQ81A5sD1UsN5Od1xKg1y4PslOyPA/E1+PPPc0yyhgPdOYqja+jxwvFrjI0ofTY0EPL2NFJ\ndmX+SSl0KDAx7qLyDNI8n+yMemx8rYkxoqLZWxMfRuZbyudFIH4zItfj/hbUr32In358ft4srqRQ\nBdr2+LBhSBFVDmiT6ydD9CgKjLaI2DEDXOCcQscplSswLnNPulD4PhHCgsXqEcadkFLPkHpCv8Zo\nqHSDLY4Qa2i7Fi0FqIC2imbqWPslBUJM2fdDFwbpDcFYtFJoLYhEwhDQCKlKxCioGGn7lj72RO1o\niiOivqCezEgSmOiSJIoQB7Rr2IQBY2p+8Wtf5eXXvsD0aMbrX76FaNgEw+z0A77197/Db/zCXX7w\np/8nt196lQ7N3Qc/Q1kLx0cNrjCUR3POvvx1Hn33W8xOjulWS+aTKUkUSmu8UtTzUzrfU5YTSGvK\n2lKVNco1yNPHRN/SdS2TyhFCxClNL4FjNM4es5aey/YD3vngz2iKuwQ/EKPG0dCtWkzRM6gVGsdq\ntSamZ1z2l8zNFGdGU6cI2lpMUdDMKwIKUqKuarxPII6YhL5rUUqI3uOouLxYMz+Z4+OSpDxKBcIg\nmLLGlBZFj7EzBr1BENAeV2mE7vP6Su/HTwLmP5Qb54VAv/NkucGXX1O1yPNJ0MNE5/W2yzchOMM1\n1wB9HKNO/Cao73Tm115lLAK8AeLXIvLxNWFPr+R1C+j7aH8L6rtCJdl7tmhJ+2SsTrk9n+R9UQod\nUm7LFwRl8xSndsnONHYZynLFmIF/fM5O6bJNgL4oEr9+u7QH8i2YX4vODz/gTz8+NzC32oCyKKVw\nNqFDT0wD4iKFPUIGj1IQhoBIjzWKwlQELXjtAJujMacw1uB7AXqCXND1NheQCFntYhp0VZCSYE2F\nIjB0A6qKWFUynx1xefEBidOcrTYGZ7PncwhC6QxK9wzDkuB7alXiVCIJWBIxJgJr1v4xE6UpK4c2\nlmp2wumdOXU95+T0Dl2Ao7Pb1LOGymrK2YSnlys+ePQ2tqx47Ytf4Vd/U/P+997i6eUF92/P+Qd/\n9Ba/+pu/RTWb8PLrd1leLZjcfQPKBxQPFPV7H/L++3+BVQLNnGG5gBQ4ufUK2jlq45AUMaaiKBSh\ntth2QRwGNus11mjYdnvSilpbQuxZDlcUOuKs4qL9LhfLJ6ihwZYaTYPElu7qEdoZJLmce8BQlBA5\np7AlroTBa8KgiGZAqZ5JY8cuRvl+NomiKCxdv8bogRBn9FEoK0vbPwHlcEWFUpHWf4QWC6MVsg9r\nYvREGVA4jLEY7T6vr/R+fFY0y8cB/Q471I3ofEutHHizcKgv3ypa9s0mwjVw19mVEL2nbm4oWp6j\nWEjXaJadZJE9+/4C4mZPj3ysPHEPkFv+3WyBXEbrgJQwsgXzuDtvJO4aaJtrYJ52bfmIsjPQkrE9\n3BbM0wjou+fEMcF5SHPrg7f63JVW9iX8cgDgOuYkKT/lYF64zMspawhRmNVT+rjBOUNdTXJ7tPSM\nFB0pJAxTrOkpnEMlhTEOLUJMAadKjIkMkhOOQ7HMbctSRKJmiJ5NWmHtBOUCxiTaNtCGc06LOxxP\nv0q/+T7rtqWwNWa8XXPW0OuItRZlE0MvrLuOqAoqAU0FpiepHpGOoNfYWc1ZdZ9Zc8rb3/sWHz16\nyPHdgWV7RdsGhre+A8rxi1//OpPiZY6P79I8mHFy9x5Pz5/x4I0v8mu/9Vv84A//J64uPL//L/8b\n9HLFF7/0GvVrr6Eu1gR3TNHcRTbPiPMZm3aBv/yQqj6hns8ZVKJbPKPvO5rZDGsMzjjKqia2kSQd\nCkNdTVkvnqBTACX4GFGiMEmYSuS202yweN2zDhfoEFG6RNsJffcUHRaj6ZkhDAMi2T5BlSuiWVNW\nE9reovxYTScdqBrrEhI8fuiwZYGWgihXBG+wdgIJ/CYwmIKi0hRFSZKBafMyUR4ShewAqc4IMVfw\nGrONpKrP8mtbkf1IS6AA/gfgbz73qE8L5j8qGn8hgPMcblxXsxzEyzcjc3meZkm79QY5Mtre5nEz\nSccOyA95ckFdP84hX77tXnQgSdyxNzcvF3LgICDXLgq730BlQDeynWlnspVdE9NuW5QCdyPRuTXb\nGqmVXPmpiTEQo8XEdKB2kd0FICc9uQ7icrCdthH59gIwgrqOB9F5PIjOP/34JGD+cX0PT4H/FngN\neBv4V4DL8Tl/E/i385+Jfw/4g5sv6lxJHzzOOLS22OSJaAo7QytHWRhit0KrQFFMKYs5Rd1hTEun\nc1Pg4AM+OgozwRnNoJcEP3C5OAfASyAGwfsAVrBlZBgS2iiyXaumMCdIamnKMxbL97ObWnQksx67\n3iRQgaKoWK83hDAgEUJIWF0wyCV1XXL/7Kuo4S7DAp6tH8OQcLO7HJ3Bm3/xFxir+Plf/ufxQeMl\ncnLnJb731lvU1TmFayjfe5d7dx/Qr1d85Vf+Od753reRKLhZyd3jW6QIP/iztzFFyZ0H91HKYGyF\n6T3LTU8xOWXZrlhddKgIm7KiKBwn5R0Krbm6esYbX/s10kmNXgwEG+jXLcMwUJsSozuSQFmWRCO0\nfUCLUMsJnVwiskAoiNGCKvPfNC3xfUfwiqQsTmkCwq2mwhYD2m44MXdZPqtQytHLmhCXROmJoWDT\nC40RXKGxriL0uaGFdQUJYRgCtihJ0QKJGC8ICaxJ9EMgmQUiU+riBBHofI+kz7RoqCObbG3I/zt/\nCPylcd2PTyNN/GGA/SLwvnY+K1ieU7PcmHvufJ/8TNd8TG7KEs31+Fkd0CzbXN+NCPvwEnATzA/V\nLDtVizqM95+PyrdUy3bkitPR71HlSHvripjBOhxsjx2Lts2kx/OiVO7SNFIrO2vboEluDArGMv4Q\nIyYGdLToEHeR+Y5m2YH5+N6v0SojeG9X8wJ6ZcfVbNdPPz4JmH9c38N/a1z/M+A/AP7GOL8G/Kvj\n+gD4e8CXb75TZ2swGm08TjVIinSxxyiHcyUxeVRISNBYXVO4GmOE2dQjsgHRrDdCiHYsoTcY3bIe\negZZ41NH4UoQzRA0Xd9RBoMrsrVu73Pv0fPLh9w6fQ0RYVaeEKPHh4RoD5K7q2iVsDTZlc0MRLWh\nVAUhrrl79jM4l7i4esrMNTTzY1TrGHTP6b0HvPfmX/CzP/91Pnr8jG/8wd/ht3//X+Tpkyu+8fe/\nwe/+7u9iVIEfhKou+cH330Sh2VytOH31K0gY2ETPqW64WC6whWUYYHW1pJhcoErL7S/+LF+Wge7i\nKSEGku959vgh6+UVejaj2yww0xn3X34FTMCtOoZnT+n7BW3fMTm+T4gt2hsc2bvd+ZapsazFMNEV\n56FE/JJNu+RocoSKmok7ow8DUVpSjPTBE7DQRapqQj2tcVaB10yaCX1fgCzo+iusy4mpECI+KGrA\nWQtRqIoGHR2DCEqVLBZPSWoY/e83+BApyimOkhRbtDY4WxMGhbGR5D9zb5ZtK6MCMMD5c4/4JP+T\nP64vyw87fxD8cZM3P4jId9tbWeLHJT4/Ljo/JEUOEPY5ID/wZnleV/68znwbnT8flTMeUePP4Tqd\nw9gjVEaSSLZdiuJue398f0y02vda3VErjDpylWmVYHNPgpj16CbGHJ2nA6plx5lv36js3vEezMcP\nJ26pFjlwTDwE9H82YP6ivocPgL8O/PZ4/L8it8T4G8C/BPw35IvA28D3gd8A/vHhixpbEEIAEbTR\nOGfRmw1+CFAlRAKSCiI92lhc4VC6QBeOYpboVwMxKfpNxVBAWeqxl2hJaTUyZC6VpOn7SAiRhCVI\npB3A9wqrK1arBUX5HpaK0s0ZhsC661G2x7iBhCLGguAznVCXU46OT/HyjPnkZZ5cvM1xXTOZ3CVF\nTxtXFO4Ot6Zn9F3LK1/4Ih+8812S7/nN3/49/u9/+H9Qz+/zl//KX2W92YBsiF5xcRnwg0cCpBh4\n83vf56VXX+HRh09o24qmiFhXcOf+EZPZnBQ7bF+gZ3Puv/5VPio+4Ol73yMMA6e3T7lzSW9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0SuRppFZSBX+fZeq7gDcq1kdNEVCCp37RkBXEYwj0ZjdY7Gk475QiF5ewd/yux031FlX/md2FGy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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f285ddfca50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
      "\n",
      "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
      "\n",
      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*A thank you to Rowland Depp for first suggesting this trick.*"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}
